We used a multiple linear regression approach to develop models predicting water, protein, and lipid content of bluegills (Lepomis macrochirus) under 4 measurement approaches varying in terms of time and money. Inputs were length, weight, relative weight, total body electrical conductivity, and water. Models predicting water and protein weights were very accurate (<5% mean error). No regression predicting lipid weight was accurate enough to be used as a predictor (>37% mean error). We then attempted to reduce inaccuracy by standardizing lipid weight 4 ways. No standardization substantially improved predictive accuracy (>30% mean error). However, our results suggest that increasing the range of values used to fit the regressions may increase precision and accuracy of prediction.